Service level agreement-aware migration for multitenant database platforms

ABSTRACT

A method for migration from a multitenant database is shown that includes building an analytical model for each of a set of migration methods based on database characteristics; predicting performance of the set of migration methods using the respective analytical model with respect to tenant service level agreements (SLAs) and current and predicted tenant workloads, where the prediction includes a migration speed and an SLA violation severity; and selecting a best migration method from the set of migration methods according to the respective predicted migration speeds and SLA violation severities.

RELATED APPLICATION INFORMATION

This application claims priority to provisional application Ser. No. 61/542,994 filed on Oct. 4, 2011, incorporated herein by reference. This application further claims priority to provisional application Ser. No. 61/543,012 filed on Oct. 4, 2011, incorporated herein by reference.

This application is related to application serial no. TBD, Attorney Docket Number 11047B (449-254) entitled “LATENCY-AWARE LIVE MIGRATION FOR MULTITENANT DATABASE PLATFORMS,” filed concurrently herewith and incorporated herein by reference.

BACKGROUND

1. Technical Field

The present invention relates to database migration and, in particular, to the migration of multitenant database platforms in such a way as to preserve tenant service level agreements.

2. Description of the Related Art

Modern cloud platforms are designed with the aim of compactly servicing many users on a large number of machines. To increase resource utilization in the presence of smaller customers, providers employ multitenancy, in which multiple users and/or applications are collocated on a single server. Ideally, each tenant on a multitenant server is both unaware of and unaffected by other tenants operating on the machine and is afforded the same level of performance it would receive on a dedicated server.

To maximize profits, providers wish to maximize the number of tenants on each server. Tenants wish to be guaranteed a certain level of performance, however, as specified by service level agreements (SLAs). An SLA may specify metrics of guaranteed service, such as system uptime and query latency. A provider balances these competing goals within the resources of a given multitenant server.

If a tenant's resource demands exceed the free capacity on a given server, other tenants on the server may be negatively impacted, causing SLA violations. Database migration may be used to relocate one or more tenants to an alternate machine, freeing resources on the crowded server. Database migration may further be used to consolidate tenants on a server with free resources, potentially freeing servers for other purposes or allowing said servers to be shut down. Migration incurs its own costs, however. There is a direct cost of copying the tenant's data to another machine, as well as penalties due to SLA violations during system downtime and human-related costs.

Most existing database systems provide tools for data export/migration in a “stop and copy” fashion. Such a solution is not practical for migrating large amounts of data, where large downtimes will be incurred. Existing live migration solutions for database systems fail to take into account the costs that such a migration may cause to a provider.

SUMMARY

A method for migration from a multitenant database is shown that includes building an analytical model for each of a set of migration methods based on database characteristics; predicting performance of the set of migration methods using the respective analytical model with respect to tenant service level agreements (SLAs) and current and predicted tenant workloads, wherein said prediction includes a migration speed and an SLA violation severity; and selecting a best migration method from the set of migration methods according to the respective predicted migration speeds and SLA violation severities.

A multitenant database system is shown that includes a processor configured to build an analytical model for each of a set of migration methods based on database characteristics, to predict performance of the set of migration methods using the respective analytical model with respect to tenant service level agreements (SLAs) and current and predicted tenant workloads, wherein said prediction includes a migration speed and an SLA violation severity, and to select a best migration method from the set of migration methods according to the respective predicted migration speeds and SLA violation severities.

These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description of preferred embodiments with reference to the following figures wherein:

FIG. 1 is a block/flow diagram for migrating a database in a multitenant system according to the present principles.

FIG. 2 is a block/flow diagram for predicting migration performance according to the present principles.

FIG. 3 is a block/flow diagram for migrating a database in a multitenant system according to the present principles.

FIG. 4 is a block/flow diagram for throttling migration speed according to the present principles.

FIG. 5 is a block/flow diagram for throttling migration speed according to the present principles.

FIG. 6 is a diagram of a migration of databases between multitenant systems according to the present principles.

FIG. 7 is a diagram of a proportional-integral-derivative controller according to the present principles.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

A practical migration solution for multitenant database systems according to the present principles includes a minimum of downtime, controlled tenant interference, and automatic management. To achieve this, the present principles employ backup tools to perform a zero-downtime live backup of a database. Further more, “migration slack” is employed, referring to resources which can be used for migration without seriously impacting existing workloads. By taking query latency into account and monitoring system performance in real-time, system performance can be guaranteed according to tenant service level agreements (SLAs) even during the live migration.

The present principles achieve live migration by using existing hot backup functionality present in database systems. Because most contemporary database systems have such hot backup functions, the present principles may be employed without changes to existing database engines and operating systems. Within the bounds of SLA guarantees, the present principles use control-theory-based strategies to automatically throttle resource usage in the migration process to achieve the fastest possible migration that does not interfere with service level guarantees.

Referring now in detail to the figures in which like numerals represent the same or similar elements and initially to FIG. 1, a high-level method for database migration is shown. At block 102, information regarding client SLAs, database characteristics, current workloads, and predicted workloads are entered as input. This information characterizes the database system as it will appear during migration, such that block 104 can predict how a migration will perform according to each of a set of migration methods. As an example, the set of migration methods may include a “stop and copy” method, while another method in the set may include a live database migration as described herein. Block 106 chooses a best method by selecting a method that will perform the migration in the shortest time without generating any SLA violations. If all migration methods tested in block 104 will generate SLA violations, then block 106 selects the migration method which generates the fewest violations. Block 106 further selects optimal migration parameters that correspond to the selected migration method. The migration method and parameters are employed in block 108 to migrate, e.g., a tenant from a first database system to a second database system.

Embodiments may include a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. A computer-usable or computer readable medium may include any apparatus that stores, communicates, propagates, or transports the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be magnetic, optical, electronic, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. The medium may include a computer-readable storage medium such as a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk, etc.

A data processing system suitable for storing and/or executing program code may include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code to reduce the number of times code is retrieved from bulk storage during execution. Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system either directly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.

Embodiments described herein may be entirely hardware, entirely software or including both hardware and software elements. In a preferred embodiment, the present invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc.

Referring now to FIG. 2, a method for modeling migration methods is shown. Block 202 builds an analytical model for each migration method to represent the resources consumed by that model. In the case of stop-and-copy, the analytical model may simply represent an amount of downtime proportional to the volume of data to be copied, using the characteristics of the database system to determine a maximum transfer rate. In the case of live migration, the analytical model may include a measurement of “migration slack,” resources that may be used for migration without impacting SLA guarantees.

As an example of determining migration slack, consider a system where the resources available in a system R must reliably exceed the combined needs of each of the n tenant, such that R≧Σ_(i=1) ^(n)T_(i). If this relation does not hold, then the server becomes overloaded and begins incurring SLA violations. Migrating a tenant also consumes some resources—usually in disk input/output for reading or writing data, but also including processing overhead, network throughput, etc. If the server is handling m migrations, the resources needed to maintain SLA guarantees becomes R≧Σ_(i=1) ^(n)T_(i)+Σ_(j=1) ^(m)M_(j). Given the constant resource allocation R and a set of workloads T, it is now possible to allocate slack resources S without incurring violations, where S=R−Σ_(i=1) ^(n)T_(i). While R remains fixed, the workloads T may change over time, such that the migration workloads M should be adjustable in real-time. Thus, it is often best to allocate resources below the slack value S to provide stability in tenant workload processing.

Block 204 uses machine learning and historic data to update the analytical models. Each model is then employed in block 206 to predict migration performance for each of the methods based on current data and predicted future workloads. This predicted performance includes the cost of SLA violations as well as migration costs such as resource usage and migration duration.

The machine learning (ML) models are used to predict the overhead cost of each migration method. For example, if the stop-and-copy method is chosen, then the ML model will look at the size of the files that need to be transferred (migrated) to the other server, the current network bandwidth, the IOPS (IO per second) that can be used for the transfer, etc. The output of the ML model is a prediction on the overhead cost of migration, such as the duration of the migration (e.g., 10 minutes) or the number of queries that are dropped during the migration (because during stop-and-copy migration, the server will be shut down).

As another example, if live migration is used, then the ML model may look at the same (or somewhat different) set of characteristics of the system, make a prediction on the migration overhead, e.g., in terms of the duration of the migration and the impact of the migration process on the query latency among all tenants on the current server. Then, based on the predictions, an appropriate (lower cost) migration method is chosen.

ML methods have two stages: a training stage and a testing stage. During the training stage, which is usually done offline, historic data are used to learn a prediction model, such as the linear regression model. During the testing stage, which is usually done online, the prediction is made based on realtime data and the model trained offline. According to the present principles, the predictive models are constantly updated by incorporating real-time data into historic data and by repeating the training stage of machine learning methods in real time. In other words, the ML model is updated (trained again) whenever new data is available. Such real-time updates can improve the model over time.

Referring now to FIG. 3, a method for live migration is provided that allows maintenance of SLA guarantees. Block 302 designates the database or databases to be migrated and selects a target server for the migration(s). Block 304 starts a hot backup of the databases to be migrated, creating a snapshot of each database. Each snapshot is transferred to its respective target server, and block 306 creates a new database from each snapshot at its respective target server. Block 308 transfers the query log that accumulated during the hot backup to the target server and replays the query log to synchronize the new database with the state of the old database. Block 310 then starts servicing queries at the new database and stops queries at the old database. At this point the old database may be purged, freeing resources at the originating server.

Referring now to FIG. 4, a method for allocating resources to migration processes M_(j) is shown. Block 402 derives an acceptable performance level based on existing SLAs. Based on this acceptable performance level, block 404 calculates slack resources. This may be performed using the formulas for S shown above. Block 406 allocates slack resources to migration processes M_(j). Block 408 monitors the system during migration to track changes in tenant workloads T_(i). Block 410 adjusts the slack resources accordingly—as tenant workloads increase, slack resources will decrease and vice versa. Processing loops back to block 406 to allocate an appropriate number of resources to the migration processes and continues to loop in this manner until the migration has completed.

Referring now to FIG. 5, a method for controlling the migration process is shown, allowing for adaptive throttling of the migration processes in response to changing resource availability. Block 502 determines available slack resources that may be employed in migration, as described above. Block 504 uses a proportional-integral-derivative (PID) controller to determine a speed of migration based on system performance. The PID controller is used to adjust system resource consumption of the migration process in block 506 by throttling disk input/output (I/O) and network bandwidth. As slack resources become available, additional resources are allocated to migration to speed the process. As tenant workloads increase and fewer resources are available, resources are taken away from the migration process to preserve SLA guarantees. Examples of throttling embodiments may include using the Linux “pv” utility to limit the amount of data passing through a given pipe. This effectively limits both CPU as well as I/O (both disk and network), because the backup process will only process the database as quickly as the “pv” utility will allow.

A PID controller is a tool in control theory for driving a system actuator such that the system stabilizes around a particular setpoint. At a high level, a PID controller operates as a continuous feedback loop that, at each timestep, adjusts the output variable (the actuator) such that a dependent variable (the process variable) converges toward a desired value (the setpoint value).

Referring now to FIG. 6, a system of database servers is shown that includes an originating server 602 and a target server 604. Each server includes a processor 606, a memory storage unit 608 (including one or more of random access memory and non-volatile storage), a multitenant database system 610, a PID controller 612, and a system monitor 614. The multitenant database system 610 at the originating server 602 includes one or more tenant databases that are to be migrated to the target server 604. System monitors 614 tracks the resources being used at each server 602/604, providing PID controller 612 and processor 606 with information regarding processor usage, memory usage, and network usage.

Referring now to FIG. 7, greater detail on the PID controller 612 is provided. At a high level, a PID controller 612 operates as a continuous feedback loop that, at each timestep, adjusts the output such that a dependent variable (the process variable 716) converges toward a desired value (the set point value 702). At each timestep, the current process variable 716 is compared to the desired setpoint 702. The new output of the controller 612 is determined by three component paths of the error 706, defined as the degree to which the process variable 716 differs from the setpoint 702 at comparator 704. The three paths are the proportional path 708, the integral path 710, and the derivative path 712. Each path is scaled by coefficients K_(p), K_(i), and K_(d) respectively. In general terms, the proportional path 708 uses the current error 706, the integral path 710 uses past errors, and the derivative path 712 predicts future error. The outputs of the paths are added at summer 714. The output at time t with error e(t) is given by the formula:

${{output}\; (t)} = {{K_{p}{e(t)}} + {K_{i}{\int_{0}^{t}{{e(\tau)}{\tau}}}} + {K_{d}{\frac{{e(t)}}{t}.}}}$

The PID controller 612 is in charge of determining the proper migration speed, so the output variable is used as the throttling speed adjustment (either speeding up or slowing down the migration). For the process variable and setpoint, one option is to target available slack using a setpoint of zero unused slack. However, to save on computation, current transaction latency may be used as an indicator of available slack. As migration speed increases, the total server load also increases. As long as the migration remains under the available slack, average transaction latency will increase only modestly as migration speed increases. However, as the slack runs out, latency will begin to display more significant increases. Thus, to ensure that slack is used without exceeding available resources, the PID controller 612 may be configured to target a given average transaction latency. The process variable 716 may therefore be set to the current average transaction latency and the setpoint 702 may be set to a target latency. This setpoint 702 represents an efficient use of available slack while still maintaining acceptable query performance. The three parameters of the different pats, K_(p), K_(i), and K_(d), may be tuned manually. A small K_(i) and a large K_(d) are used to set a reaction speed to changes in migration speed to prevent overshooting the optimal speed, allowing the overall latency to stabilize.

Having described preferred embodiments of a system and method for SLA-aware migration for multitenant database platforms (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments disclosed which are within the scope of the invention as outlined by the appended claims. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims. 

What is claimed is:
 1. A method for migration from a multitenant database, comprising: building an analytical model for each of a set of migration methods based on database characteristics; predicting performance of the set of migration methods using the respective analytical model with respect to tenant service level agreements (SLAs) and current and predicted tenant workloads, wherein said prediction includes a migration speed and an SLA violation severity; and selecting a best migration method from the set of migration methods according to the respective predicted migration speeds and SLA violation severities.
 2. The method of claim 1, wherein the set of migration methods comprises live database migration.
 3. The method of claim 1, further comprising updating the analytical models using historical data.
 4. The method of claim 1, wherein a migration method having a lowest SLA violation severity is selected.
 5. The method of claim 4, wherein the migration method having a highest predicted migration speed is selected if the migration methods have equal predicted SLA violation severities.
 6. The method of claim 1, further comprising selecting a set of optimal parameters for the selected migration method based on the selected method's predicted performance and current and predicted tenant workloads.
 7. The method of claim 1, further comprising selecting one or more tenants to migrate based on tenant resource usage.
 8. The method of claim 1, further comprising updating the analytical models with machine learning.
 9. A multitenant database system, comprising: a processor configured to build an analytical model for each of a set of migration methods based on database characteristics, to predict performance of the set of migration methods using the respective analytical model with respect to tenant service level agreements (SLAs) and current and predicted tenant workloads, wherein said prediction includes a migration speed and an SLA violation severity, and to select a best migration method from the set of migration methods according to the respective predicted migration speeds and SLA violation severities. 